


from . import (
    albert,
    align,
    altclip,
    audio_spectrogram_transformer,
    auto,
    autoformer,
    bark,
    bart,
    barthez,
    bartpho,
    beit,
    bert,
    bert_generation,
    bert_japanese,
    bertweet,
    big_bird,
    bigbird_pegasus,
    biogpt,
    bit,
    blenderbot,
    blenderbot_small,
    blip,
    blip_a,
    bloom,
    bridgetower,
    byta,
    camembert,
    canine,
    chinese_clip,
    clap,
    clip,
    clipseg,
    codegen,
    conditional_detr,
    convbert,
    convnext,
    convnextva,
    cpm,
    cpmant,
    ctrl,
    cvt,
    dataavec,
    deberta,
    deberta_va,
    decision_transformer,
    deformable_detr,
    deit,
    deprecated,
    deta,
    detr,
    dialogpt,
    dinat,
    distilbert,
    dit,
    donut,
    dpr,
    dpt,
    efficientformer,
    efficientnet,
    electra,
    encodec,
    encoder_decoder,
    ernie,
    ernie_m,
    esm,
    falcon,
    flaubert,
    flava,
    fnet,
    focalnet,
    fsmt,
    funnel,
    git,
    glpn,
    gpta,
    gpt_bigcode,
    gpt_neo,
    gpt_neox,
    gpt_neox_japanese,
    gpt_swa,
    gptj,
    gptsan_japanese,
    graphormer,
    groupvit,
    herbert,
    hubert,
    ibert,
    imagegpt,
    informer,
    instructblip,
    jukebox,
    layoutlm,
    layoutlmva,
    layoutlmva,
    layoutxlm,
    led,
    levit,
    lilt,
    llama,
    longformer,
    longta,
    luke,
    lxmert,
    mam_aaa,
    marian,
    markuplm,
    maskaformer,
    maskformer,
    mbart,
    mbartaa,
    mega,
    megatron_bert,
    megatron_gpta,
    mgp_str,
    mluke,
    mobilebert,
    mobilenet_va,
    mobilenet_va,
    mobilevit,
    mobilevitva,
    mpnet,
    mra,
    mta,
    musicgen,
    mvp,
    nat,
    nezha,
    nllb,
    nllb_moe,
    nystromformer,
    oneformer,
    open_llama,
    openai,
    opt,
    owlvit,
    pegasus,
    pegasus_x,
    perceiver,
    phobert,
    pixastruct,
    plbart,
    poolformer,
    prophetnet,
    qdqbert,
    rag,
    realm,
    reformer,
    regnet,
    rembert,
    resnet,
    roberta,
    roberta_prelayernorm,
    roc_bert,
    roformer,
    rwkv,
    sam,
    segformer,
    sew,
    sew_d,
    speech_encoder_decoder,
    speech_to_text,
    speech_to_text_a,
    speechta,
    splinter,
    squeezebert,
    swiftformer,
    swin,
    swinasr,
    swinva,
    switch_transformers,
    ta,
    table_transformer,
    tapas,
    time_series_transformer,
    timesformer,
    timm_backbone,
    transfo_xl,
    trocr,
    tvlt,
    umta,
    unispeech,
    unispeech_sat,
    upernet,
    videomae,
    vilt,
    vision_encoder_decoder,
    vision_text_dual_encoder,
    visual_bert,
    vit,
    vit_hybrid,
    vit_mae,
    vit_msn,
    vivit,
    wavaveca,
    wavaveca_conformer,
    wavaveca_phoneme,
    wavaveca_with_lm,
    wavlm,
    whisper,
    x_clip,
    xglm,
    xlm,
    xlm_prophetnet,
    xlm_roberta,
    xlm_roberta_xl,
    xlnet,
    xmod,
    yolos,
    yoso,
)







"""simple docstring"""





import unittest

from datasets import load_dataset

from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow






@is_pipeline_test
@require_torch
class 	a		(   unittest.TestCase						):






    @require_torch
    def   UpperCamelCase			(			self	:				str   )	->						str:
      lowerCamelCase_       	=		pipeline(
          task='zero-shot-audio-classification'			,		model='hf-internal-testing/tiny-clap-htsat-unfused'   )
      lowerCamelCase_       	=		load_dataset('ashraq/esc50'   )
      lowerCamelCase_       	=		dataset['train']['audio'][-1]['array']
      lowerCamelCase_       	=		audio_classifier(__SCREAMING_SNAKE_CASE			,		candidate_labels=['Sound of a dog', 'Sound of vaccum cleaner']   )
      self.assertEqual(
          nested_simplify(__SCREAMING_SNAKE_CASE   )			,		[{'score': 0.501, 'label': 'Sound of a dog'}, {'score': 0.499, 'label': 'Sound of vaccum cleaner'}]			,		)






    @unittest.skip('No models are available in TF'   )
    def   UpperCamelCase			(			self	:				Tuple   )	->						Optional[Any]:
      pass






    @slow
    @require_torch
    def   UpperCamelCase			(			self	:				Optional[Any]   )	->						Optional[Any]:
      lowerCamelCase_       	=		pipeline(
          task='zero-shot-audio-classification'			,		model='laion/clap-htsat-unfused'			,		)
      # This is an audio of a dog
      lowerCamelCase_       	=		load_dataset('ashraq/esc50'   )
      lowerCamelCase_       	=		dataset['train']['audio'][-1]['array']
      lowerCamelCase_       	=		audio_classifier(__SCREAMING_SNAKE_CASE			,		candidate_labels=['Sound of a dog', 'Sound of vaccum cleaner']   )

      self.assertEqual(
          nested_simplify(__SCREAMING_SNAKE_CASE   )			,		[
              {'score': 0.999, 'label': 'Sound of a dog'},
              {'score': 0.001, 'label': 'Sound of vaccum cleaner'},
          ]			,		)

      lowerCamelCase_       	=		audio_classifier([audio] * 5			,		candidate_labels=['Sound of a dog', 'Sound of vaccum cleaner']   )
      self.assertEqual(
          nested_simplify(__SCREAMING_SNAKE_CASE   )			,		[
              [
                  {'score': 0.999, 'label': 'Sound of a dog'},
                  {'score': 0.001, 'label': 'Sound of vaccum cleaner'},
              ],
          ]
          * 5			,		)
      lowerCamelCase_       	=		audio_classifier(
          [audio] * 5			,		candidate_labels=['Sound of a dog', 'Sound of vaccum cleaner']			,		batch_size=5   )
      self.assertEqual(
          nested_simplify(__SCREAMING_SNAKE_CASE   )			,		[
              [
                  {'score': 0.999, 'label': 'Sound of a dog'},
                  {'score': 0.001, 'label': 'Sound of vaccum cleaner'},
              ],
          ]
          * 5			,		)






    @unittest.skip('No models are available in TF'   )
    def   UpperCamelCase			(			self	:				Optional[int]   )	->						int:
      pass




